CIO Business Intelligence

DECEMBER 20, 2024

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

Cloudera

JUNE 9, 2022

With the rapid increase of cloud services where data needs to be delivered (data lakes, lakehouses, cloud warehouses, cloud streaming systems, cloud business processes, etc.), controlling distribution while also allowing the freedom and flexibility to deliver the data to different services is more critical than ever. .

Data Collection 94

Data Collection IoT Data Lake Unstructured Data 94

CIO Business Intelligence

NOVEMBER 27, 2024

That’s still important, but not always as relevant to the unstructured and semi-structured data gen AI deals with, which will also have a lot more variation. Data quality for AI needs to cover bias detection, infringement prevention, skew detection in data for model features, and noise detection. asks Friedman.

Enterprise 141

Enterprise Data Quality Structured Data Modeling 141

O'Reilly on Data

NOVEMBER 13, 2019

While Jonas applauds such inquiry and thinking deeply about the social ramifications of AI research, he is concerned the questions might be reinventing the wheel: “The data collection itself often has serious ramifications that we’ve all been wrestling with for 15 years.

Machine Learning 282

Machine Learning Data Collection Modeling IT 282

TDAN

AUGUST 17, 2021

If you are planning on using predictive algorithms, such as machine learning or data mining, in your business, then you should be aware that the amount of data collected can grow exponentially over time.

Predictive Modeling 115

Predictive Modeling Modeling Data mining Big Data 115

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

O'Reilly on Data

DECEMBER 10, 2019

We can collect many examples of what we want the program to do and what not to do (examples of correct and incorrect behavior), label them appropriately, and train a model to perform correctly on new inputs. Nor are building data pipelines and deploying ML systems well understood. Instead, we can program by example.

Software 338

Software Machine Learning Risk Data-driven 338

O'Reilly on Data

JULY 28, 2020

You must detect when the model has become stale, and retrain it as necessary. The Marketing team built the first model, but because it was from marketing, the model optimized for CTR and lead conversion. Nonetheless, building a superior feature pipeline or model architecture will always be worthwhile.

Marketing 364

Marketing Experimentation Metrics Testing 364

CIO Business Intelligence

NOVEMBER 19, 2024

The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both. Imagine that you’re a data engineer. The data is spread out across your different storage systems, and you don’t know what is where. What does the next generation of AI workloads need?

Management 116

Management Unstructured Data Deep Learning Metadata 116

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 364

Management Experimentation B2B Machine Learning 364

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 363

Management Machine Learning Experimentation Metrics 363

Rocket-Powered Data Science

JULY 6, 2021

Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. One type of implementation of a content strategy that is specific to data collections are data catalogs. Data catalogs are very useful and important.

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

Rocket-Powered Data Science

MARCH 19, 2021

Focus on specific data types: e.g., time series, video, audio, images, streaming text (such as social media or online chat channels), network logs, supply chain tracking (e.g., Dynamic sense-making, insights discovery, next-best-action response, and value creation is essential when data is being acquired at an enormous rate.

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

CIO Business Intelligence

FEBRUARY 20, 2025

The data retention issue is a big challenge because internally collected data drives many AI initiatives, Klingbeil says. With updated data collection capabilities, companies could find a treasure trove of data that their AI projects could feed on. of their IT budgets on tech debt at that time.

Insurance 108

Insurance Cost-Benefit Unstructured Data Data Lake 108

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Machine Learning 277

Machine Learning Risk Management Statistics 277

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 349

Enterprise Deep Learning Data Governance Risk 349

Smart Data Collective

MARCH 3, 2023

Then, you make adjustments based on what’s working within your business model— and what isn’t. It’s important to get an objective look at where there are shortcomings in your business model. That’s where modern data tools come in. Using Data to Find Shortcomings & Opportunities No business model is perfect.

Modeling 111

Modeling Cost-Benefit Software Data Collection 111

Smart Data Collective

AUGUST 4, 2022

However, there are some downsides to shifting towards a data-driven healthcare delivery model. One of the biggest issues is that the system can break down when healthcare organizations have trouble accessing data. Their data delivery models become disrupted, which hinders the entire organization.

Data-driven 130

Data-driven Modeling Cost-Benefit Big Data 130

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

O'Reilly on Data

OCTOBER 22, 2020

Taking the time to work this out is like building a mathematical model: if you understand what a company truly does, you don’t just get a better understanding of the present, but you can also predict the future. Since I work in the AI space, people sometimes have a preconceived notion that I’ll only talk about data and models.

Consulting 363

Consulting Risk Cost-Benefit Modeling 363

O'Reilly on Data

JANUARY 28, 2019

The problems with consent to data collection are much deeper. It comes from medicine and the social sciences, in which consenting to data collection and to being a research subject has a substantial history. We really don't know how that data is used, or might be used, or could be used in the future.

Insurance 247

Insurance Metadata Data Collection Marketing 247

Smart Data Collective

JUNE 29, 2021

The algorithms will evaluate all the data available regarding you and interpret it in the context of the big data collected worldwide. The thought of insurance companies toying with your data can feel a little frightening. What does this mean for consumers?

Insurance 133

Insurance Modeling Big Data IT 133

IBM Big Data Hub

SEPTEMBER 7, 2023

Today we are announcing our latest addition: a new family of IBM-built foundation models which will be available in watsonx.ai , our studio for generative AI, foundation models and machine learning. Collectively named “Granite,” these multi-size foundation models apply generative AI to both language and code.

Modeling 101

Modeling Risk Unstructured Data Enterprise 101

Dataiku

OCTOBER 19, 2021

Between energy diversity, climate challenges, and growth in electricity consumption, energy producers and suppliers must constantly optimize their processes and anticipate demand in order to adjust their offers, a strategy based on massive data collection and the deployment of AI solutions.

Modeling 105

Modeling Data Collection Data Science Optimization 105

David Menninger's Analyst Perspectives

SEPTEMBER 24, 2024

AI governance should address a number of issues, including data privacy, bias in data and models, drift in model accuracy, hallucinations and toxicity. Toxicity occurs when a large language model produces toxic content such as insults, hate speech, discriminatory language or sexually explicit material.

Testing 173

Testing Modeling Enterprise Risk 173

CIO Business Intelligence

JULY 11, 2022

To meet the customer demands of a digital-first business model, retailers need to address their critical digital infrastructure and rethink network design and cybersecurity. Retailers can leverage the SASE framework to develop overarching network strategies and address the new types of cyber risks within omnichannel models.

Modeling 98

Modeling IoT Cost-Benefit Management 98

KDnuggets

JANUARY 30, 2023

The ChatGPT Cheat Sheet • ChatGPT as a Python Programming Assistant • How to Select Rows and Columns in Pandas Using [ ],loc, iloc,at and.iat • 5 Free Data Science Books You Must Read in 2023 • From Data Collection to Model Deployment: 6 Stages of a Data Science Project

Data Science 140

Data Science Data Collection Modeling 140

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

SEPTEMBER 12, 2023

The UK government’s Ecosystem of Trust is a potential future border model for frictionless trade, which the UK government committed to pilot testing from October 2022 to March 2023. The models also reduce private sector customs data collection costs by 40%.

Testing 83

Testing Modeling Cost-Benefit Consulting 83

CIO Business Intelligence

JULY 24, 2024

OpenAI announced on Wednesday a new approach to AI development, one that it said “aligns models to behave safely without extensive human data collection,” although some have raised concerns about AI interacting with AI. This model guides the AI by signaling desirable actions.

Recreation/Entertainment 132

Recreation/Entertainment Data Collection Modeling Reporting 132

CIO Business Intelligence

MARCH 4, 2025

From AI models that boost sales to robots that slash production costs, advanced technologies are transforming both top-line growth and bottom-line efficiency. A major stumbling block is often quality data collection. CEOs, CIOs and CFOs are finding that deep tech is actively driving business innovation and profitability.

Technology 118

Technology ROI Cost-Benefit Manufacturing 118

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 167

Analytics Machine Learning Data Science Data Collection 167

Smart Data Collective

MARCH 24, 2020

The relationship between performance parameters and factors for predicting performance is involved in complex nonlinear relationships, so the areas of data collection should be comprehensive. A selection of information sources, data acquisition procedures, information processing algorithms. Data collection.

Big Data 121

Big Data Internet Publishing and Broadcasting Broadcasting Data Collection 121

CIO Business Intelligence

AUGUST 9, 2024

Privacy protection The first step in AI and gen AI projects is always to get the right data. “In In cases where privacy is essential, we try to anonymize as much as possible and then move on to training the model,” says University of Florence technologist Vincenzo Laveglia. “A A balance between privacy and utility is needed.

Risk 138

Risk Measurement Data Lake Data Collection 138

Smart Data Collective

MAY 16, 2022

Data management systems provide a systematic approach to information storage and retrieval and help in streamlining the process of data collection, analysis, reporting, and dissemination. It also helps in providing visibility to data and thus enables the users to make informed decisions.

Management 136

Management Data mining Data Warehouse Data Collection 136

Rocket-Powered Data Science

MAY 21, 2024

To see this, look no further than Pure Storage , whose core mission is to “ empower innovators by simplifying how people consume and interact with data.” RAG is the essential link between two things: (a) the general large language models (LLMs) available in the market, and (b) a specific organization’s local knowledge base.

Cost-Benefit 239

Cost-Benefit Unstructured Data Enterprise Technology 239

Cloudera

DECEMBER 6, 2024

Therefore, the next 10%, which are small language models, are going to come into play. Data collectives are going to merge over time, and industry value chains will consolidate and share information. But 85% accuracy in the supply chain means you have no manufacturing operations. 85% accuracy in finance can put you in jail.

Manufacturing 82

Manufacturing Forecasting Metadata Data Processing 82